[EMNLP2015]Effective Approaches to Attention-based Neural Machine Translation
neural machine translation有以下优点： (1) 有能力生成很长的词序列 (2) 因为不需要存储巨大的短语词表，所以需要很小的内存 (3) 解码很容易 A： 介绍了两种attention模型，其共同点是在每一步decoding时hidden state h t 都作为输入参与计算c t (1)global attention 在生成target word y t 时， in
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相关文章
- 1. Paper：Effective Approaches to Attention-based Neural Machine Translation
- 2. 【论文笔记】Effective Approaches to Attention-based Neural Machine Translation
- 3. 论文笔记(Attention 2)-----Effective Approaches to Attention-based Neural Machine Translation
- 4. Effective Approaches to Attention-based Neural Machine Translation 学习笔记
- 5. 多标签分类：Effective Approaches to Attention-based Neural Machine Translation
- 6. Effective Approaches to Attention-based Neural Machine Translation之每日一篇
- 7. Effective Approaches to Attention-based Neural Machine Translation论文笔记
- 8. 【CS224n】Neural Machine Translation with Seq2Seq
- 9. Neural Machine Translation by Jointly Learning to Align and....
- 10. Neural Machine Translation by Jointly Learning to Align and Translate